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A survey of few shot learning based on intelligent perception

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2020年 41卷

作者： Song, Chuang Zhao, Jiajia Wang, Kang Liang, Xinkai Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Computer and Technique Fudan University Shanghai200433 China

Few-shot learning refers to using only a small amount of supervision information of the target class to train the machine learning model. Due to its practical values, recent advances in few-shot learning by academia and industry have made significant contributions. However, there were few reviews on this issue in China. This paper systematically summarizes and explores the few-shot learning algorithms and the object detection algorithms based on few-shot learning. Firstly, the problem definition of few-shot learning is given, and its connections with other classic machine learning problems are also enumerated. Meanwhile, the theoretical challenges of the problem of few-shot learning are explained. Then, we summarize the image classification based on few-shot learning, and analyze its representative works. Based on this, we focus on the problem of few-shot object detection, especially the problem of zero-shot object detection, and analyze the existing research works in detail. Finally, we look forward to the future development of few-shot learning in terms of problem setting, theoretical research, implementation technology, and application scenarios based on the advantages and disadvantages of the existing methods. It is expected to provide inspirations for the subsequent research works in this field. © 2020, Beihang University Aerospace Knowledge Press. All right reserved.

关键词： Image classification

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A Human-Robot Skill Transfer Framework of Mobile Medical Robots for Autonomous Motion with Teaching by Demonstration

A Human-Robot Skill Transfer Framework of Mobile Medical Rob...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiehao Li Junzheng Wang Shoukun Wang Hui Peng Qi Wen Longbin Zhang Meina Lin Politecnico di Milano Milan Italy State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China BioMEx Center KTH Mechanics KTH Royal Institute of Technology Stockholm Sweden Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

The accuracy of autonomous motion is the main challenge for mobile medical robots, especially running in narrow space conditions to transport the casualties in the hospital. This article mainly centers on the human-robot transmission of the mobile medical robot for narrow space autonomous motion. In order to enable mobile medical robots to autonomously and smoothly transport hospital casualties through narrow aisles, in this paper, we present a robot skill transfer technology, namely a human-robot skill transfer framework of mobile medical robots for autonomous motion with teaching by demonstration, which is that a smooth optimal path is planned through the human motion, and then the Kinect sensor is used to detect human bone movement to learn its motion trajectory so as to achieve trajectory tracking control. Meanwhile, using the Remote Center of Motion (RCM) constraint to minimize the error between the actual trajectory of the medical robot and the tracking reference path to control the motion trajectory accurately, human-computer interactive mobile medical robots can smoothly pass through narrow channels. Through research and analysis, it is demonstrated that the mobile medical robot proposed in this paper has great feasibility, which is of great reference value in the field of medical rescue.

关键词： Medical robotics Education Trajectory Mobile robots Tracking Hospitals

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Topology Optimization Design of LEO Satellite Network

Topology Optimization Design of LEO Satellite Network

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第三十八届中国控制会议

作者： Jian Tai Junting Lv XiANDe Wu Ting Song Qiang Zhang Yanbing Xiang Jun Sun College of Aerospace and Civil Engineering Harbin Engineering University Shanghai Key Laboratory of Space Intelligent Control Technology Beijing Institute of Spacecraft System Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory

This paper establishes a satellite network topology optimization model based on satellite orbital dynamics, establishes constraints from two aspects of satellite geometric visibility and satellite elevation constraints in the concept of inter-satellite links, and finally solves the above optimization problems by using simulated annealing algorithm, and presents a time-varying satellite network topology optimization method. In the actual simulation, we give a satellite network with multiple satellites and optimize it. The optimal solution of the network configuration is obtained, and a shortest inter-satellite transmission path from signal start star to signal termination star based on time-varying is solved. For this inter-satellite link, the connectivity of inter-satellite link and the fault tolerance of satellite network are analyzed, which proves the feasibility and validity of the optimization method.

关键词： LEO satellite network topology optimization inter-satellite link simulated annealing algorithm

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A class of elliptic mixed boundary value problems with (p,q)-Laplacian: existence, comparison and optimal control

arXiv

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arXiv 2023年

作者： Zeng, Shengda Migórski, Stanislaw Tarzia, Domingo A. Zou, Lang Nguyen, Van Thien Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30348 Poland College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University in Krakow Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Depto. Matemática FCE Universidad Austral Paraguay 1950 RosarioS2000FZF Argentina CONICET Argentina School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City210000 China Department of Mathematics FPT University Education zone Hoa Lac High Tech Park Km29 Thang Long Highway Thach That ward Hanoi Viet Nam

The paper deals with two nonlinear elliptic equations with (p, q)-Laplacian and the Dirichlet-Neumann-Dirichlet (DND) boundary conditions, and Dirichlet-Neumann-Neumann (DNN) boundary conditions, respectively. Under mild hypotheses, we prove the unique weak solvability of the elliptic mixed boundary value problems. Then, a comparison and a monotonicity results for the solutions of elliptic mixed boundary value problems are established. We examine a convergence result which shows that the solution of (DND) can be approached by the solution of (DNN). Moreover, two optimal control problems governed by (DND) and (DNN), respectively, are considered, and an existence result for optimal control problems is obtained. Finally, we provide a result on asymptotic behavior of optimal controls and system states, when a parameter tends to *** Codes 35J25, 35J66, 35J92, 49J20, 35Bxx © 2023, CC BY.

关键词： Nonlinear equations

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A Infrared Cloud Image Simulation Method for Cloud Segmentation Network Training

A Infrared Cloud Image Simulation Method for Cloud Segmentat...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Hang Qi Jianquan Yuan Lei Li Jun Ren Jie Liang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Institute of Mechanical and Electrical Engineering Beijing China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

Due to the difficulty of acquiring real aerial infrared cloud images, we propose an efficient way to set up cloud simulation data set by particle system and texture synthesis, in order to alleviate the problem of data shortage. The deep network for cloud segmentation is trained on our simulation image data set, substantially decreasing the demand for real aerial cloud images. When testing on the real image data set, a satisfactory segmentation result with 79.64% has been reached which can meet the usage requirements. Our method can make the deep cloud segmentation network training feasible despite of the real image shortage, reducing the cost of data acquisition.

关键词： Clouds Image segmentation Data models Training Shape Image edge detection Force

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A summary of the development of cooperative and intelligent technology for multi-UAV systems

A summary of the development of cooperative and intelligent ...

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2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Zhou, Huan Su, Qian Fu, Wenxing Xu, Cheng Zheng, Meiyun Yang, Jingguang Aeronautical Engineering College Air Force Engineering University Xi'an China School of Astronautics Northwest Polytechnical University Xi'an China Unmanned System Research Institute Northwest Polytechnical University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Marine Science and Technology Northwest Polytechnical University Xi'an China Aviation Maintenance Sergeant School of Air Force Engineering University Xinyang China

ISBN: (纸本)9781728158594

In the process of formation cooperative autonomous flight and mission execution, multi-UAV systems have the characteristics of large disturbance, strong coupling of multi variables, high degree of uncertainty and complex cooperative factors, so there are many difficulties in the design of cooperative and intelligent control system. How to effectively develop the cooperative and intelligent technology of multi-UAV systems has become an urgent problem in the field of unmanned system and artificial intelligence. This paper first introduces the key technologies involved in UAV system at present, and then analyzes the challenges faced by the development of UAV system. Finally, it focuses on the development trend of multi-UAV systems cooperation and intelligent technology in the future. The research results have strong theoretical guidance significance for the cooperation and intelligent development of multi-UAV systems in China. © 2019 IEEE.

关键词： Flight control systems

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

关键词：

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An Improved Robust Adaptive Kalman Filtering Algorithm

An Improved Robust Adaptive Kalman Filtering Algorithm

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第三十八届中国控制会议

作者： Liuyang Jiang Wenxing Fu Hai Zhang Zheng Li Longyun Chi School of Automation Science and Electrical Engineering Beihang University Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory National Airspace Management Center

Robust adaptive Kalman filter(RAKF) is a method that can not only compensate for the model errors but also resist the measurement outliers. The method reduces the influence of the measurement outliers on the filtering system by reducing the weight of the abnormal observation. After the processing of robust estimation, all the errors are attributed to the inaccuracy of the model. Since the robust estimation is not theoretically quantitative that it can only reduce the observation error to a certain extent, the remaining observation errors are compensated as the model errors, resulting in new estimation errors. To address this problem, a method for detecting the model error is proposed as a trigger condition for RAKF to compensate for the model error,and this constitutes an improved robust adaptive Kalman filter(IRAKF) algorithm. The new algorithm performs the model errors compensation stricter than the original method. The efficacy of the improved algorithm is demonstrated via a traffic running testing with GPS/INS tight integration navigation system. Simulation results indicate that the improved algorithm can effectively reduce the inaccurate adaptive procedure and obtain relatively accurate and stable filtering results.

关键词： Adaptive Kalman Filter Robust Estimation Improved Model Errors Tight Integrated Navigation

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